Liquid crystalline networks (LCNs) are versatile functional materials because of the unique properties of the liquid crystalline (LC) molecules, e.g., self-organization, reversible phase transition, and macroscopic orientation under external fields. The coupling between LC molecules and polymer networks allows these remarkable properties to be transferred to the bulk material, which results in a number of functional LCNs that are thermally responsive and are able to change their shape reversibly upon temperature cycling
Shape memory polymers (SMP) are smart materials that can recover their original shape from a deformed state under external stimuli. SMPs generally consist of cross-linked polymer networks that determine the permanent shape of the material, and switching segments that are capable of being oriented and solidified to fix a temporary shape. The shape recovery is driven by the entropic force of the switching domains, which tend to gain entropy and return to the random conformation during phase transitions, such as glass transition, liquid crystalline (LC) transition, and melting transition.
In recent years, using light to induce shape change in a material has received much interest. However, the currently known photoresponsive LCNs, such as those based on either polysiloxane or polyacrylate chemistry, are generally incapable of undergoing a reversible phase transition, thereby affecting the potential functionality of these materials. In addition, these siloxane and acrylate-based LCNs generally cannot be reprocessed because of their covalently fixed cross-linked structure, which makes it impossible to reshape or repair the material.